#!/usr/bin/python
# -*- coding:utf-8 -*-
# @FileName : DL4_test6.py
# Author    : myh

import torch
from torch import nn
from d2l import torch as d2l


def init_weights(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, std=0.01)
# def dropout_layer(X, dropout):
#     assert 0 <= dropout <= 1
#     # 在本情况中，所有元素都被丢弃
#     if dropout == 1:
#         return torch.zeros_like(X)
#     # 在本情况中，所有元素都被保留
#     if dropout == 0:
#         return X
#     mask = (torch.rand(X.shape) > dropout).float()
#     return mask * X / (1.0 - dropout)
#
#
if __name__ == '__main__':
    #     X = torch.arange(16, dtype=torch.float32).reshape((2, 8))
    #     print(X)
    #     print(dropout_layer(X, 0.))
    #     print(dropout_layer(X, 0.5))
    #     print(dropout_layer(X, 1.))
    num_inputs, num_outputs, num_hiddens1, num_hiddens2 = 784, 10, 256, 256
    dropout1, dropout2 = 0.2, 0.5
    #
    #
    #     class Net(nn.Module):
    #         def __init__(self, num_inputs, num_outputs, num_hiddens1, num_hiddens2,
    #                      is_training=True):
    #             super(Net, self).__init__()
    #             self.num_inputs = num_inputs
    #             self.training = is_training
    #             self.lin1 = nn.Linear(num_inputs, num_hiddens1)
    #             self.lin2 = nn.Linear(num_hiddens1, num_hiddens2)
    #             self.lin3 = nn.Linear(num_hiddens2, num_outputs)
    #             self.relu = nn.ReLU()
    #
    #         def forward(self, X):
    #             H1 = self.relu(self.lin1(X.reshape((-1, self.num_inputs))))
    #             # 只有在训练模型时才使用dropout
    #             if self.training == True:
    #                 # 在第一个全连接层之后添加一个dropout层
    #                 H1 = dropout_layer(H1, dropout1)
    #             H2 = self.relu(self.lin2(H1))
    #             if self.training == True:
    #                 # 在第二个全连接层之后添加一个dropout层
    #                 H2 = dropout_layer(H2, dropout2)
    #             out = self.lin3(H2)
    #             return out
    #
    #
    # net = Net(num_inputs, num_outputs, num_hiddens1, num_hiddens2)
    num_epochs, lr, batch_size = 10, 0.5, 256
    loss = nn.CrossEntropyLoss(reduction='none')
    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
    #     trainer = torch.optim.SGD(net.parameters(), lr=lr)
    #     d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)
    #     d2l.plt.show()

    # net = nn.Sequential(nn.Flatten(),
    #         nn.Linear(784, 256),
    #         nn.ReLU(),
    #         # 在第一个全连接层之后添加一个dropout层
    #         nn.Dropout(dropout1),
    #         nn.Linear(256, 256),
    #         nn.ReLU(),
    #         # 在第二个全连接层之后添加一个dropout层
    #         nn.Dropout(dropout2),
    #         nn.Linear(256, 10))

    # 不加暂退
    net = nn.Sequential(nn.Flatten(),
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Linear(256, 10))

    net.apply(init_weights)

    trainer = torch.optim.SGD(net.parameters(), lr=lr)
    d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)





